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<a href="_p_c_a_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Principal Component Analysis</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      T. Glasmachers, C. Igel</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        2010, 2011</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * </span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> *</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> */</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_TRAINER_PCA_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#define SHARK_ALGORITHMS_TRAINER_PCA_H</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_d_l_l_support_8h.html">shark/Core/DLLSupport.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_linear_model_8h.html">shark/Models/LinearModel.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_trainer_8h.html" title="Abstract Trainer Interface.">shark/Algorithms/Trainers/AbstractTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment"></span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///  \brief Principal Component Analysis</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  The Principal Component Analysis, also known as</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///  Karhunen-Loeve transformation, takes a symmetric</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  \f$ n \times n \f$ matrix \f$ A \f$ and uses its decomposition</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///  \f$</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///      A = \Gamma \Lambda \Gamma^T,</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///  \f$</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///  where \f$ \Lambda \f$ is the diagonal matrix of eigenvalues</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///  of \f$ A \f$ and \f$ \Gamma \f$ is the orthogonal matrix</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///  with the corresponding eigenvectors as columns.</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///  \f$ \Lambda \f$ then defines a successive orthogonal rotation</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///  that maximizes the variances of the coordinates, i.e. the</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///  coordinate system is rotated in such a way that the correlation</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">///  between the new axes becomes zero. If there are \f$ p \f$ axes,</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">///  the first axis is rotated in a way that the points on the new axis</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">///  have maximum variance. Then the remaining \f$ p - 1 \f$ axes are</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">///  rotated such that a another axis covers a maximum part of the rest</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">///  variance, that is not covered by the first axis. After the</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">///  rotation of \f$ p - 1 \f$ axes, the rotation destination of axis</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">///  no. \f$ p \f$ is fixed.  An application for PCA is the reduction</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">///  of dimensions by skipping the components with the least</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">///  corresponding eigenvalues/variances. Furthermore, the eigenvalues</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">///  may be rescaled to one, resulting in a whitening of the data.</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">/// \ingroup unsupervised_trainer</span></div>
<div class="foldopen" id="foldopen00073" data-start="{" data-end="};">
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html">   73</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_p_c_a.html" title="Principal Component Analysis.">PCA</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_unsupervised_trainer.html" title="Superclass of unsupervised learning algorithms.">AbstractUnsupervisedTrainer</a>&lt;LinearModel&lt;&gt; &gt;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>{</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_unsupervised_trainer.html" title="Superclass of unsupervised learning algorithms.">AbstractUnsupervisedTrainer&lt;LinearModel&lt;&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_unsupervised_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735a91ee8e8923d2abb8c39354c99194c27f">   78</a></span>    <span class="keyword">enum</span> <a class="code hl_enumeration" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735">PCAAlgorithm</a> { <a class="code hl_enumvalue" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735aa46a1b1a70f542e1569589fb42528d6d">STANDARD</a>, <a class="code hl_enumvalue" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735aa87e03fd3af865af990a4fdd3f0551b3">SMALL_SAMPLE</a>, <a class="code hl_enumvalue" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735a91ee8e8923d2abb8c39354c99194c27f">AUTO</a> };</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment"></span> </div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    /// Constructor.</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// The parameter defines whether the model should also</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    /// whiten the data.</span></div>
<div class="foldopen" id="foldopen00083" data-start="{" data-end="}">
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a439006abce1b7c09792b7c574a2d709e">   83</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_p_c_a.html#a439006abce1b7c09792b7c574a2d709e">PCA</a>(<span class="keywordtype">bool</span> whitening = <span class="keyword">false</span>) </div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    : <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a09654e9c3e1eba7522ab8e389457fbd0" title="normalize variance yes/no">m_whitening</a>(whitening){</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a2987f11dfe9ee6e005e93ea7fca138ec" title="whether to use design matrix or its transpose for building covariance matrix">m_algorithm</a> = <a class="code hl_enumvalue" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735a91ee8e8923d2abb8c39354c99194c27f">AUTO</a>;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    };<span class="comment"></span></div>
</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span><span class="comment">    /// Constructor.</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    /// The parameter defines whether the model should also</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    /// whiten the data.</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    /// The eigendecomposition of the data is stored inthe PCA object.</span></div>
<div class="foldopen" id="foldopen00091" data-start="{" data-end="}">
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a655563cb672cb1a67071e73fdf11a24c">   91</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_p_c_a.html#a655563cb672cb1a67071e73fdf11a24c">PCA</a>(<a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; inputs, <span class="keywordtype">bool</span> whitening = <span class="keyword">false</span>) </div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    : <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a09654e9c3e1eba7522ab8e389457fbd0" title="normalize variance yes/no">m_whitening</a>(whitening){</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a2987f11dfe9ee6e005e93ea7fca138ec" title="whether to use design matrix or its transpose for building covariance matrix">m_algorithm</a> = <a class="code hl_enumvalue" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735a91ee8e8923d2abb8c39354c99194c27f">AUTO</a>;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <a class="code hl_function" href="classshark_1_1_p_c_a.html#a52747dc693e68fe208daf290dc6b5f54">setData</a>(inputs);</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    };</div>
</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment"></span> </div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00098" data-start="{" data-end="}">
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a900a33903800136e115a2d208067cba9">   98</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_p_c_a.html#a900a33903800136e115a2d208067cba9" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;PCA&quot;</span>; }</div>
</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment"></span> </div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment">    /// If set to true, the encoded data has unit variance along</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// the new coordinates.</span></div>
<div class="foldopen" id="foldopen00103" data-start="{" data-end="}">
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a13027f51bff74d5b7a39d4040a9aa403">  103</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#a13027f51bff74d5b7a39d4040a9aa403">setWhitening</a>(<span class="keywordtype">bool</span> whitening) {</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a09654e9c3e1eba7522ab8e389457fbd0" title="normalize variance yes/no">m_whitening</a> = whitening;</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    }</div>
</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment"></span> </div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// Train the model to perform PCA. The model must be a</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">    /// LinearModel object with offset, and its output dimension</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    /// defines the number of principal components</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    /// represented. The model returned is the one given by the</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// econder() function (i.e., mapping from the original input</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// space to the PCA coordinate system).</span></div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#aeae45267c5bc7a3cda484b203a1f15be">  113</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#aeae45267c5bc7a3cda484b203a1f15be">train</a>(<a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; inputs) {</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        std::size_t m = model.<a class="code hl_function" href="classshark_1_1_linear_model.html#a9eeb86bc2b2c822fa5b9617e80a98d91" title="Returns the shape of the output.">outputShape</a>().<a class="code hl_function" href="classshark_1_1_shape.html#ad36fc62c674b01150cc5addab9dcc38d">numElements</a>(); <span class="comment">///&lt; reduced dimensionality</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        <a class="code hl_function" href="classshark_1_1_p_c_a.html#a52747dc693e68fe208daf290dc6b5f54">setData</a>(inputs);   <span class="comment">// compute PCs</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        <a class="code hl_function" href="classshark_1_1_p_c_a.html#a58785ce7beacbbb5b38ee50baab18430">encoder</a>(model, m); <span class="comment">// define the model </span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    }</div>
</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span> </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment"></span> </div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    //! Sets the input data and performs the PCA. This is a</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    //! computationally costly operation. The eigendecomposition</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    //! of the data is stored inthe PCA object.</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a52747dc693e68fe208daf290dc6b5f54">  123</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#a52747dc693e68fe208daf290dc6b5f54">setData</a>(<a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; inputs);</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment"></span> </div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    //! Returns a model mapping the original data to the</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    //! m-dimensional PCA coordinate system.</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a58785ce7beacbbb5b38ee50baab18430">  127</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#a58785ce7beacbbb5b38ee50baab18430">encoder</a>(<a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a>&amp; model, std::size_t m = 0);</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment"></span> </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    //! Returns a model mapping encoded data from the</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    //! m-dimensional PCA coordinate system back to the</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment">    //! n-dimensional original coordinate system.</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#af637c896bf9edd55a3184a135a4334e2">  132</a></span><span class="comment"></span>    <a class="code hl_define" href="_d_l_l_support_8h.html#a54b73283f7f70b27fbd8ac5d4621827f" title="Defines SHARK_COMPILE_DLL.">SHARK_EXPORT_SYMBOL</a> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#af637c896bf9edd55a3184a135a4334e2">decoder</a>(<a class="code hl_class" href="classshark_1_1_linear_model.html" title="Linear Prediction with optional activation function.">LinearModel&lt;&gt;</a>&amp; model, std::size_t m = 0);</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment"></span> </div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment">    /// Eigenvalues of last training. The number of eigenvalues</span></div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="comment">    //! is equal to the minimum of the input dimensions (i.e.,</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span><span class="comment">    //! number of attributes) and the number of data points used</span></div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment">    //! for training the PCA.</span></div>
<div class="foldopen" id="foldopen00138" data-start="{" data-end="}">
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a8a377bad66488acb59ab44a3c7ee21ea">  138</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_p_c_a.html#a8a377bad66488acb59ab44a3c7ee21ea">eigenvalues</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4fa5f42a2412a520846a57323bdf0376" title="eigenvalues">m_eigenvalues</a>;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment">    /// Returns ith eigenvalue.</span></div>
<div class="foldopen" id="foldopen00142" data-start="{" data-end="}">
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a495d0d796ea14d28c6b56643c271ed9b">  142</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_p_c_a.html#a495d0d796ea14d28c6b56643c271ed9b" title="Returns ith eigenvalue.">eigenvalue</a>(std::size_t i)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( i &lt; <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4a784a8145636a7f0a4ca7b196900e71" title="number of training data points">m_l</a> );</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keywordflow">if</span>( i &lt; <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4fa5f42a2412a520846a57323bdf0376" title="eigenvalues">m_eigenvalues</a>.size()) </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4fa5f42a2412a520846a57323bdf0376" title="eigenvalues">m_eigenvalues</a>(i);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="keywordflow">return</span> 0.;</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    }</div>
</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="comment"></span> </div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment">    //! Eigenvectors of last training. The number of eigenvectors</span></div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span><span class="comment">    //! is equal to the minimum of the input dimensions (i.e.,</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="comment">    //! number of attributes) and the number of data points used</span></div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span><span class="comment">    //! for training the PCA.</span></div>
<div class="foldopen" id="foldopen00153" data-start="{" data-end="}">
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a0ef11fb10a4914cf2a60da03512a4230">  153</a></span><span class="comment"></span>    RealMatrix <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_p_c_a.html#a0ef11fb10a4914cf2a60da03512a4230">eigenvectors</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#ae49e453937be746737aa2d13b38e16a1" title="eigenvectors">m_eigenvectors</a>;</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>    }</div>
</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment"></span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">    /// mean of last training</span></div>
<div class="foldopen" id="foldopen00158" data-start="{" data-end="}">
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a752eeeb52e068a200891e1419b367033">  158</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_p_c_a.html#a752eeeb52e068a200891e1419b367033" title="mean of last training">mean</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a1a4657da75acb0e29002f9d50cc6a298" title="mean value">m_mean</a>;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>    }</div>
</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a09654e9c3e1eba7522ab8e389457fbd0">  163</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a09654e9c3e1eba7522ab8e389457fbd0" title="normalize variance yes/no">m_whitening</a>;          <span class="comment">///&lt; normalize variance yes/no</span></div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#ae49e453937be746737aa2d13b38e16a1">  164</a></span>    RealMatrix <a class="code hl_variable" href="classshark_1_1_p_c_a.html#ae49e453937be746737aa2d13b38e16a1" title="eigenvectors">m_eigenvectors</a>; <span class="comment">///&lt; eigenvectors</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a4fa5f42a2412a520846a57323bdf0376">  165</a></span>    RealVector <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4fa5f42a2412a520846a57323bdf0376" title="eigenvalues">m_eigenvalues</a>;  <span class="comment">///&lt; eigenvalues</span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a1a4657da75acb0e29002f9d50cc6a298">  166</a></span>    RealVector <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a1a4657da75acb0e29002f9d50cc6a298" title="mean value">m_mean</a>;     <span class="comment">///&lt; mean value</span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#afaf333706ff30d74832d6e18c01c8dfa">  168</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_p_c_a.html#afaf333706ff30d74832d6e18c01c8dfa" title="number of attributes">m_n</a>;           <span class="comment">///&lt; number of attributes</span></div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a4a784a8145636a7f0a4ca7b196900e71">  169</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a4a784a8145636a7f0a4ca7b196900e71" title="number of training data points">m_l</a>;           <span class="comment">///&lt; number of training data points</span></div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span> </div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"><a class="line" href="classshark_1_1_p_c_a.html#a2987f11dfe9ee6e005e93ea7fca138ec">  171</a></span>    <a class="code hl_enumeration" href="classshark_1_1_p_c_a.html#ad3b450f29c9b4b265f0d16039cac8735">PCAAlgorithm</a> <a class="code hl_variable" href="classshark_1_1_p_c_a.html#a2987f11dfe9ee6e005e93ea7fca138ec" title="whether to use design matrix or its transpose for building covariance matrix">m_algorithm</a>;  <span class="comment">///&lt; whether to use design matrix or its transpose for building covariance matrix</span></div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>};</div>
</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span> </div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span> </div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>}</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="preprocessor">#endif </span><span class="comment">// SHARK_ML_TRAINER_PCA_H</span></div>
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